Bibliographies thématiques / Copy-move attack

Littérature scientifique sur le sujet « Copy-move attack »

Auteur : Grafiati
Publié le 10 mars 2023
Mis à jour le 11 mars 2023

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Articles de revues sur le sujet "Copy-move attack"

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Imannisa Rahma, Firstyani, et Ema Utami. « Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms ». Telematika 15, no 1 (28 février 2022) : 1–13. http://dx.doi.org/10.35671/telematika.v15i1.1322.

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One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.
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Liu, Bo, et Chi Man Pun. « HSV Based Image Forgery Detection for Copy-Move Attack ». Applied Mechanics and Materials 556-562 (mai 2014) : 2825–28. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2825.

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As the great development of digital photography and relevant post-processing technology, digital image forgery becomes easily in terms of operating thus may be improperly utilized in news photography in which any forgery is strictly prohibited or the other scenario, for instance, as an evidence in the court. Therefore, digital image forgery detection technique is needed. In this paper, attention has been focused on copy-move forgery that one region is copied and then pasted onto other zones to create duplication or cover something in an image. A novel method based on HSV color space feature is proposed and experimental result will be given and it shows the effectiveness and accurateness of proposed methodology.
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Amerini, I., L. Ballan, R. Caldelli, A. Del Bimbo et G. Serra. « A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery ». IEEE Transactions on Information Forensics and Security 6, no 3 (septembre 2011) : 1099–110. http://dx.doi.org/10.1109/tifs.2011.2129512.

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Warif, Nor Bakiah Abd, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Rosli Salleh et Fazidah Othman. « SIFT-Symmetry : A robust detection method for copy-move forgery with reflection attack ». Journal of Visual Communication and Image Representation 46 (juillet 2017) : 219–32. http://dx.doi.org/10.1016/j.jvcir.2017.04.004.

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Rajalakshmi, C., Al M. Germanus et R. Balasubramanian. « Copy move forgery detection using key point localized super pixel based on texture features ». Computer Optics 43, no 2 (avril 2019) : 270–76. http://dx.doi.org/10.18287/2412-6179-2019-43-2-270-276.

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The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.
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Mahmood, Toqeer, Tabassam Nawaz, Aun Irtaza, Rehan Ashraf, Mohsin Shah et Muhammad Tariq Mahmood. « Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images ». Mathematical Problems in Engineering 2016 (2016) : 1–13. http://dx.doi.org/10.1155/2016/8713202.

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Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.
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TM, Shashidhar, et KB Ramesh. « Reviewing the Effectivity Factor in Existing Techniques of Image Forensics ». International Journal of Electrical and Computer Engineering (IJECE) 7, no 6 (1 décembre 2017) : 3558. http://dx.doi.org/10.11591/ijece.v7i6.pp3558-3569.

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Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.
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Genç, Ziya Alper, Gabriele Lenzini et Daniele Sgandurra. « Cut-and-Mouse and Ghost Control ». Digital Threats : Research and Practice 2, no 1 (mars 2021) : 1–23. http://dx.doi.org/10.1145/3431286.

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To protect their digital assets from malware attacks, most users and companies rely on antivirus (AV) software. AVs’ protection is a full-time task against malware: This is similar to a game where malware, e.g., through obfuscation and polymorphism, denial of service attacks, and malformed packets and parameters, tries to circumvent AV defences or make them crash. However, AVs react by complementing signature-based detection with anomaly or behavioral analysis, and by using OS protection, standard code, and binary protection techniques. Further, malware counter-acts, for instance, by using adversarial inputs to avoid detection, and so on. In this cat-and-mouse game, a winning strategy is trying to anticipate the move of the adversary by looking into one’s own weaknesses, seeing how the adversary can penetrate them, and building up appropriate defences or attacks. In this article, we play the role of malware developers and anticipate two novel moves for the malware side to demonstrate the weakness in the AVs and to improve the defences in AVs’ side. The first one consists in simulating mouse events to control AVs, namely, to send them mouse “clicks” to deactivate their protection. We prove that many AVs can be disabled in this way, and we call this class of attacks Ghost Control . The second one consists in controlling whitelisted applications, such as Notepad, by sending them keyboard events (such as “copy-and-paste”) to perform malicious operations on behalf of the malware. We prove that the anti-ransomware protection feature of AVs can be bypassed if we use Notepad as a “puppet” to rewrite the content of protected files as a ransomware would do. Playing with the words, and recalling the cat-and-mouse game, we call this class of attacks Cut-and-Mouse . We tested these two attacks on 29 AVs, and the results show that 14 AVs are vulnerable to Ghost Control attack while all 29 AV programs tested are found vulnerable to Cut-and-Mouse . Furthermore, we also show some weaknesses in additional protection mechanisms of AVs, such as sandboxing and CAPTCHA verification. We have engaged with the affected AV companies, and we reported the disclosure communication with them and their responses.
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Manoharan, J. Samuel. « Design of an Intelligent Approach on Capsule Networks to Detect Forged Images ». September 2021 3, no 3 (2 octobre 2021) : 205–21. http://dx.doi.org/10.36548/jtcsst.2021.3.004.

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Forgeries have recently become more prevalent in the society as a result of recent improvements in media generation technologies. In real-time, modern technology allows for the creation of a forged version of a single image obtained from a social network. Forgery detection algorithms have been created for a variety of areas; however they quickly become obsolete as new attack types exist. This paper presents a unique image forgery detection strategy based on deep learning algorithms. The proposed approach employs a convolutional neural network (CNN) to produce histogram representations from input RGB color images, which are then utilized to detect image forgeries. With the image separation method and copy-move detection applications in mind, the proposed CNN is combined with an intelligent approach and histogram mapping. It is used to detect fake or true images at the initial stage of our proposed work. Besides, it is specially designed for performing feature extraction in image layer separation with the help of CNN model. To capture both geographical and histogram information and the likelihood of presence at the same time, we use vectors in our dynamic capsule networks to detect the forgery kernels from reference images. The proposed research work integrates the intelligence with a feature engineering approach in an efficient manner. They are well-known and efficient in the identification of forged images. The performance metrics such as accuracy, recall, precision, and half total error rate (HTER) are computed and tabulated with the graph plot.
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Wang, Xiaofeng, Guanghui He, Chao Tang, Yali Han et Shangping Wang. « Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks ». International Journal of Pattern Recognition and Artificial Intelligence 30, no 03 (22 février 2016) : 1655008. http://dx.doi.org/10.1142/s0218001416550089.

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A novel image passive forensics method for copy-move forgery detection is proposed. The proposed method combines block matching technology and feature point matching technology, and breaks away from the general framework of the visual feature-based approach that used local visual feature such as SIFT and followed by a clustering procedure to group feature points that are spatially close. In our work, image keypoints are extracted using Harris detector, and the statistical features of keypoint neighborhoods are used to generate forensics features. Then we proposed a new forensics features matching approach, in which, a region growth technology and a mismatch checking approach are developed to reduce mismatched keypoints and improve detected accuracy. We also develop a duplicate region detection method based on the distance frequency of corresponding keypoint pairs. The proposed method can detect duplicate regions for high resolution images. It has higher detection accuracy and computation efficiency. Experimental results show that the proposed method is robust for content-preserving manipulations such as JPEG compression, gamma adjustment, filtering, luminance enhancement, blurring, etc.
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Thèses sur le sujet "Copy-move attack"

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Li, Yuan Man. « SIFT-based image copy-move forgery detection and its adversarial attacks ». Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952093.

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AMERINI, IRENE. « Image Forensics : sourceidentification and tamperingdetection ». Doctoral thesis, 2010. http://hdl.handle.net/2158/520262.

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Chapitres de livres sur le sujet "Copy-move attack"

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Studiawan, Hudan, Rahmat Nazali Salimi et Tohari Ahmad. « Forensic Analysis of Copy-Move Attack with Robust Duplication Detection ». Dans Advances in Intelligent Systems and Computing, 404–13. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_39.

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Soni, Badal, Pradip K. Das, Dalton Meitei Thounaojam et Debalina Biswas. « Copy–Move Attack Detection from Digital Images : An Image Forensic Approach ». Dans Advances in Intelligent Systems and Computing, 69–76. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9683-0_8.

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Islam, Mohammad Manzurul, Joarder Kamruzzaman, Gour Karmakar, Manzur Murshed et Gayan Kahandawa. « Passive Detection of Splicing and Copy-Move Attacks in Image Forgery ». Dans Neural Information Processing, 555–67. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04212-7_49.

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Anand, Vijay, Mohammad Farukh Hashmi et Avinash G. Keskar. « A Copy Move Forgery Detection to Overcome Sustained Attacks Using Dyadic Wavelet Transform and SIFT Methods ». Dans Intelligent Information and Database Systems, 530–42. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05476-6_54.

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Mehta, Sonam, et Pragya Shukla. « An Efficient Technique for Passive Image Forgery Detection Using Computational Intelligence ». Dans Advances in Digital Crime, Forensics, and Cyber Terrorism, 31–45. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3942-5.ch003.

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This chapter proposes a scheme for the identification of copy-move forgery by inducing adaptive over-segmentation and matching of feature points with the help of discrete cosine transform (DCT). Copy-move forging is an image tampering technique that involves concealing undesired things or recreating desirable elements within the same image to create modified tampered images. Traditional methods added a large number of false matches. To conquer this problem, a new algorithm is proposed to incorporate an adaptive threshold method. So, the block feature matching mechanism is used, and the matching feature blocks classify the feature points using patch matching and Hough transform. Forged regions are detected with the help of the newly proposed algorithm. The results of the proposed method show that it can substantially reduce the number of false matches that lead to improvements in both performance and computational costs. This demonstrates the suggested algorithm's resistance against a variety of known attacks. Comparative results are presented for a better evaluation.
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Neena Raj N.R. et Shreelekshmi R. « A Secure Image Authentication Scheme with Tamper Localization and Recovery ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210199.

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This paper proposes a secure image authentication scheme that can locate the tampered regions, recover the lost contents and hide application-specific sensitive data. In this scheme, an encrypted watermark that comprises tamper localization, recovery and application-specific information is placed in the selected pixels, which is extracted and decrypted to identify the tampered regions, recover the tampered regions approximate to original image contents and extract the hidden data. The watermark is highly secure and sensitive to any modification in the image. The proposed scheme ensures lossless recovery of the original image and data from an untampered image. The experimental results show that this scheme generates watermarked image of high quality and has high resistance to copy-move, image splicing, vector quantization and collage attacks. As compared with state-of-the-art schemes, the proposed scheme provides better recovered image quality under extensive tampering.
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Actes de conférences sur le sujet "Copy-move attack"

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Franc, Igor, et Milos Stojmenovic. « Techniques of image manipulation and detection of copy-move attack ». Dans 2012 20th Telecommunications Forum Telfor (TELFOR). IEEE, 2012. http://dx.doi.org/10.1109/telfor.2012.6419535.

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Soni, Badal, Pradip K. Das et Dalton Meitei Thounaojam. « Improved Block-based Technique using SURF and FAST Keypoints Matching for Copy-Move Attack Detection ». Dans 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474093.

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Liu, Bo, et Chi-Man Pun. « A SIFT and local features based integrated method for copy-move attack detection in digital image ». Dans 2013 IEEE International Conference on Information and Automation (ICIA). IEEE, 2013. http://dx.doi.org/10.1109/icinfa.2013.6720415.

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Islam, Mohammad Manzurul, Gour Karmakar, Joarder Kamruzzaman, Manzur Murshed, Gayan Kahandawa et Nahida Parvin. « Detecting Splicing and Copy-Move Attacks in Color Images ». Dans 2018 Digital Image Computing : Techniques and Applications (DICTA). IEEE, 2018. http://dx.doi.org/10.1109/dicta.2018.8615874.

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Wang, Xiaofeng, Xiaoni Zhang, Zhen Li et Shangping Wang. « A DWT-DCT Based Passive Forensics Method for Copy-Move Attacks ». Dans 2011 3rd International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2011. http://dx.doi.org/10.1109/mines.2011.98.

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Sadu, Chiranjeevi, et Pradip K. Das. « A Detection Method for Copy-Move Forgery Attacks in Digital Images ». Dans TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON). IEEE, 2022. http://dx.doi.org/10.1109/tencon55691.2022.9977490.

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Nair, Gokul, Kaustubh Venkatesh, Dipankar Sen et Reena Sonkusare. « Identification of Multiple Copy-move Attacks in Digital Images using FFT and CNN ». Dans 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9580052.

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Abbas, Muhammad Naveed, Mohammad Samar Ansari, Mamoona Naveed Asghar, Nadia Kanwal, Terry O'Neill et Brian Lee. « Lightweight Deep Learning Model for Detection of Copy-Move Image Forgery with Post-Processed Attacks ». Dans 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2021. http://dx.doi.org/10.1109/sami50585.2021.9378690.

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Sharma, Shashank, et Sunita V. Dhavale. « A review of passive forensic techniques for detection of copy-move attacks on digital videos ». Dans 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS ). IEEE, 2016. http://dx.doi.org/10.1109/icaccs.2016.7586396.

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